/*
 * This file is part of the LIRE project: http://lire-project.net
 * LIRE is free software; you can redistribute it and/or modify
 * it under the terms of the GNU General Public License as published by
 * the Free Software Foundation; either version 2 of the License, or
 * (at your option) any later version.
 *
 * LIRE is distributed in the hope that it will be useful,
 * but WITHOUT ANY WARRANTY; without even the implied warranty of
 * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
 * GNU General Public License for more details.
 *
 * You should have received a copy of the GNU General Public License
 * along with LIRE; if not, write to the Free Software
 * Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA  02111-1307  USA
 *
 * We kindly ask you to refer the any or one of the following publications in
 * any publication mentioning or employing Lire:
 *
 * Lux Mathias, Savvas A. Chatzichristofis. Lire: Lucene Image Retrieval -
 * An Extensible Java CBIR Library. In proceedings of the 16th ACM International
 * Conference on Multimedia, pp. 1085-1088, Vancouver, Canada, 2008
 * URL: http://doi.acm.org/10.1145/1459359.1459577
 *
 * Lux Mathias. Content Based Image Retrieval with LIRE. In proceedings of the
 * 19th ACM International Conference on Multimedia, pp. 735-738, Scottsdale,
 * Arizona, USA, 2011
 * URL: http://dl.acm.org/citation.cfm?id=2072432
 *
 * Mathias Lux, Oge Marques. Visual Information Retrieval using Java and LIRE
 * Morgan & Claypool, 2013
 * URL: http://www.morganclaypool.com/doi/abs/10.2200/S00468ED1V01Y201301ICR025
 */

package net.semanticmetadata.lire.aggregators;

import net.semanticmetadata.lire.classifiers.Cluster;

/**
 * This class implements the clusterForFeature method for both BOVW and VLAD
 * Created by Nektarios on 03/06/2015.
 *
 * @author Nektarios Anagnostopoulos, nek.anag@gmail.com
 * (c) 2015 by Nektarios Anagnostopoulos
 */
public abstract class AbstractAggregator implements Aggregator {

    /**
     * Returns the index of the cluster with the min distance between a feature and a codebook.
     * @param f is the feature.
     * @param clustersArray is the codebook.
     * @return index of the cluster.
     */
    protected int clusterForFeature(double[] f, Cluster[] clustersArray) {
        double distance, min = clustersArray[0].getDistance(f);
        int result = 0;
        for (int i = 1; i < clustersArray.length; i++) {
            distance = clustersArray[i].getDistance(f);
            if (distance < min) {
                min = distance;
                result = i;
            }
        }
        return result;
    }
}
